european nearshoring index - is eastern europe attractive ... · additionally, many western...

Volume 8 | Issue 3 | 2019

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CENTRAL EUROPEAN BUSINESS REVIEW

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EUROPEAN NEARSHORING INDEX – IS EASTERN EUROPE ATTRACTIVE FOR SWISS IT FIRMS? ———————————————————————————————————————

Keller, F., Zoller-Rydzek, B. ——————————————————————————————————————— Florian Keller, Benedikt Zoller-Rydzek / ZHAW School of Management and Law, Center for EMEA

Business, Stadthausstrasse 14, 8400 Winterthur, Switzerland. Email: [email protected],

[email protected]

Abstract

The main goal of this paper is to identify the major factors for the decision of Swiss IT service

firms to nearshore their locations and to quantify their relative importance. Moreover, we

develop an IT Nearshoring Index ranking the attractiveness of different European regions.

We use a quantitative survey of 56 Swiss IT service firms that are either actively engaging in

nearshoring or planning to nearshore parts of their business. Using the survey, we identified

five main factors for the nearshoring location decision of Swiss IT firms: economic, labour,

institutional, social and location. We pin down the relative importance (weights) of the

aforementioned factors using the survey results and expert interviews. The labour factors

(including labour costs on the one and the availability of skilled IT workforce on the other side)

proved to be most important. We use the obtained weights to construct a (weighted) IT

Nearshoring Index. Based on the IT Nearshoring Index, we find that in contrast to general

belief, the most attractive locations cannot be found in Eastern Europe, but in Southern UK

or Western Germany. The first is due to their high availability of IT workforce, the latter due

to their cultural and geographical proximity. Eastern European regions can base their

competitive advantage on offering attractive labour costs, but this cannot make up for the

disadvantage of greater cultural and geographical distance to Switzerland.

Keywords: nearshoring, IT services, location choice, MNEs

JEL Classification: F23, L22, L23, L86, M16

Introduction

In the last several years, we have seen a rise in IT nearshoring activities of Swiss companies.

For example, two major Swiss Banks are running large service and IT centres in Poland.

Credits Suisses’ “Centers of Excellence” in Wroclaw and Warsaw employ around 4’500

people, and UBS runs “Shared Service Centers” in Krakow and Wroclaw with around 3’500

employees (Imwinkelried, 2017). Kündig & Müller (2019) report that Swisscom, the Swiss

national telecom provider, announced the opening of an IT centre in the Netherlands. The

discrepancies in these examples lead to the question of which European region would provide

the best basis for a future nearshoring project of a Swiss IT services company.


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36 CENTRAL EUROPEAN BUSINESS REVIEW

Whereas offshoring was for a long time mainly associated with manufacturing, the dramatic

change of information technology in the last decades made the offshoring of services, and

specifically IT services, much more feasible. Additionally, many Western European countries

and Switzerland specifically faced a severe shortage of skilled IT workers, which also led to

an upward pressure on the wage costs of IT workers. Many companies resorted to

outsourcing and offshoring parts of their IT departments to locations with more favorable

conditions. However, after a first wave of offshoring services especially to India (Agrawal et

al., 2010), many companies became aware that geographical and cultural distance is a

relevant success factor for IT offshoring projects. Therefore, they began to offshore to

countries that combined a large supply of skilled IT workers at attractive labour costs and

geographical as well as cultural proximity. This development was named nearshoring

(Carmel & Abbott, 2007; Stetten et al., 2010; Ellram, 2013). Consequently, Egger et al. (2018)

haven proven empirically that most European firms have affiliated firms in close proximity to

their headquarters. The IT sector in Switzerland is no exception to this trend. Litzke et al.

(2015) have shown that not only are 42% of companies already using nearshoring strategies,

but they are also considering the strategy for the future compared to offshoring.

In this context it is crucial to understand the different factors and determinants for the decision

of Swiss IT firms to nearshore their locations. This leads to the following two research

question:

RQ1: What are factors determining and shaping the offshoring location decision of Swiss IT

firms?

RQ2: What is the relative importance these factors?

To answer these research questions we employ a firm survey to identify the most important

nearshoring factors. Additionally, we evaluate the relative importance of these factors and

construct an IT Nearshoring Index. The IT Nearshoring Index pins down regions within

Europe that are more attractive for Swiss IT firms and helps to point out the underlying

reasons. Understanding regional heterogeneity will enable firms to strategically select their

nearshoring location, to maximize their comparative advantage, and to gain competitiveness

in global markets.

The examples of Swiss banks UBS and Credit Suisse described above, as well as the

experience from previous manufacturing nearshoring, lets us assume that the Eastern

European countries are prime locations for service offshoring.1 This leads to the following

hypothesis:

The Eastern countries are the most attractive nearshoring locations (in Europe) for Swiss IT

service firms.

We use the aforementioned IT Nearshoring Index to test this hypothesis.

1 We follow the defiinition of the United Nations Statistical Division in which Eastern Europe consists of Belarus,

Bulgaria, Czech Republic, Hungary, Poland, Republic of Moldova, Romania, the Russian Federation, Slovakia and

Ukrain. Additionally we add the three Baltic states: Estonia, Latvia and Lithuania.

http://vdf.ch/index.php?route=product/author&author_id=3309


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In the literature, a firm’s decision to off- or nearshore manufacturing traditionally depends on

basic economic factors such as wage rates in the possible destination countries/regions or

geographical distance between the headquarters and the possible location of the affiliate

(Markusen, 2001; Navaretti & Veneables, 2004). Offshoring of services is fundamentally

different from the offshoring of manufacturing, as most services are provided face-to-face and

often require specific knowledge, which is hard to codify. To this end, Nonaka & Takeuchi

(1995), Audia et al. (2001), and Bray (2007) point out that the transferability of knowledge is

very important for firms and their organizational structure. Non-codifiable knowledge can be

seen as intangible assets for firms. Better access to these intangible assets generates a

comparative advantage for firms (Dierkes et al., 2003; Buckley & Casson, 2016). Siegel et al.

(2013) argue that cultural distance is an important obstacle for firms to transfer intangible

assets or non-codifiable knowledge to foreign affiliates. Thus, cultural components are an

important factor for firms when considering the location of an affiliate (Jarvenpaa & Leidner,

1998; Boh et al., 2007). Therefore, the aforementioned IT Nearshoring Index does not only

incorporate classical economic factors, such as market opportunities or corporate taxes in

possible nearshoring destinations, but also includes soft components such as cultural

distance measures, which are even more relevant for off- and nearshoring.

In our survey we find that Swiss IT firms’ decision for location is mainly driven by labour

market factors, more specifically by the availability of skilled IT workers. General economic

factors, such as market size or taxes, are not as important. Intuitively, the local market size

for IT services does not matter as much as the ability to offshore these services which in and

of itself makes them easily tradeable. Thus, these services do not need to be consumed in

local markets, making them less dependent on local market size. Moreover, cultural factors

and specifically cultural distance are important for Swiss IT service firms’ nearshoring

decision, reflecting the importance of non-codifiable knowledge for IT services. In general,

our overall IT Nearshoring Index indicates that the most attractive regions are either

neighbouring to Switzerland or are easily reachable metropolitan areas, i.e. London, Berlin,

Hamburg and Madrid. Geographically we observe two clusters: the South of the UK around

London and Western Germany are most attractive. While the advantage of Western Germany

is rooted mainly in geographical and cultural closeness, the UK cluster dominates through its

availability of highly skilled IT workers. Contrary to public perception, Eastern Europe is not

overly attractive for Swiss IT firms looking to nearshore. This is due to social factors and

geographical distance which constitute a great barrier for Swiss IT firms. Although wage

levels are relatively low in almost all Eastern European regions, the supply of highly skilled

IT workers varies dramatically between Eastern European regions. Consistently, we find that

the most attractive Eastern European regions are the ones that are able to accumulate a

substantial IT work force and are easy to reach from Switzerland, such as Central Poland

(Warsaw).

The remainder of the paper is structured as follows. After the introduction in Section 1, we

describe the construction of the overall IT Nearshoring Index and its five sub-indices in

Section 2. Additionally, we give details about the data collection process. We answer our

research questions as well. In Section 3 we describe each sub-index in more detail. Section

4 gives an overview of the data and presents the actual nearshoring index and its different

components. We test our hypothesis and briefly explain the impact of a possible Brexit on

our IT Nearshoring Index. Finally, in Section 5 we conclude.


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1 Methodical approach and data collection

The IT Nearshoring Index reflects various dimensions of the decision of Swiss IT service firms

to nearshore some of their activities. To identify the relative importance of different

determinants, the Center of European Business at the ZHAW School of Management and

Law in cooperation with swissICT, the largest association in the Swiss IT industry, and ISSS

(Information Security Society Switzerland), the leading association in the Swiss IT Security

industry, conducted a survey among Swiss IT service firms. The questionnaire used in the

firm survey consisted of 34 questions regarding general information about the firm (number

of employees, revenues, etc.), the nearshoring experience (currently engaging in

nearshoring, in which countries, etc.), and, most importantly, the importance of different

factors for the nearshoring decision of the firm.2 We received 56 high quality responses. 82%

(46 firms) of the responding firms are actively engaging in nearshoring in various destinations

in Europe. About 41% of all firms have more than 100 employees and a quarter have

revenues exceeding USD 50 million. In the questionnaire, firms were asked to rate the

importance of different factors for their nearshoring decision on a scale of 1 to 7, with 1 being

not important at all and 7 very important. We asked the respondents to directly rate the

importance of factors such as the economic potential of a region, the availability of skilled IT

workers in a region, and the cultural distance between a region and Switzerland. Based on

these questions and the answers, we were able to answer the RQ1 and to identify 5 major

determinants of the nearshoring decisions of firms. We commonly refer to these determinants

as pillars of our IT Nearshoring Index: economic, labour, institutional, social and location

pillar. Each pillar is created by weighting several variables that are associated with the

respective pillar, i.e. matched by the underlying survey question. The overall IT Nearshoring

Index consists of a weighted average of these five pillars. Figure 1 graphically depicts the

stylized structure of the IT Nearshoring Index.

Figure 1 | Construction of the IT Nearshoring Index

We used data from various sources, focusing on the Eurostat Nomenclature of Territorial

Units for Statistics (NUTS) level 1 regions. The variables were chosen to match the underlying

2 Most of these questions were multiple choice questions.

INDEX

PILLARS

QUESTIONS

VARIABLES

IT Nearshoring Index

Economic

Taxe

sC

orp

ora

te

tax

rate

s

Eco

no

mic

d

eve

lop

me

nt

Reg

ion

al

GD

P

Labour

Ava

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ility

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f IT

w

ork

ers

Shar

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f IT

w

ork

ers

Wag

es

Avg

. IT

wag

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sts

Institutional

Euro

pea

n

inte

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ion

EU

mem

ber

shi

p

Go

vern

me

nt

qu

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Reg

ion

al

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ent

qu

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y

Social

Cu

ltu

ral

dis

tan

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ult

ura

l d

ista

nce

Per

son

al

con

tact

sN

um

ber

of

exp

ats

Location

Rea

chab

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ista

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in

km

IT

infr

astr

uct

ure

Bro

adb

and

ac

cess

%

Source: authors


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39

questions of the survey. For example, firms were asked how important the market potential

of the region is, which was meant to correspond with the variable of GDP and GDP per capita

growths. Similarly, the reachability of a region was proxied by the distance in kilometers from

Berne (Switzerland) and the number of airport passengers in the region. For each question,

we identified a broad set of variables that could be used to describe the attractiveness of a

region for IT service firms.

Focusing on Eurostat NUTS 1 regions allows a much finer distinction within countries, as

regional variation and differences can be an important factor for a firm’s decision to nearshore

(Abbott & Jones, 2012). If variables were only available at NUTS 2 level, we aggregated

them, using population weights where appropriate. If only country level variables were

available, we applied them to all NUTS 1 regions within the country. We always considered

the last available year in the data, which most of the time was 2017 or 2018. Our main data

source was the Eurostat regional database, which gives a broad set of variables at various

NUTS levels. We provide an Online Appendix with detailed variable description and source

information.3 In our data set some observations were missing for certain regions, which we

filled in through a machine learning approach. Specifically, we employed multi-equation

imputation of missing observations within each pillar using a random forest approach (Wulff

& Ejlskov, 2017). In total we have 50 variables assigned to the 5 pillars and we cover 115

NUTS 1 regions in Europe. While the Eurostat regional database is the most comprehensive

source for detailed regional statistics, its coverage of countries is far from complete. Some

interesting nearshoring destinations like Serbia, Ukraine, or Albania are missing due to the

lack of reported data or differences in the data collection. However, as we will see later on,

the main competitive advantages of these regions are not that crucial for Swiss IT companies.

Therefore, we are confident, that these regions would not rank high in our index.

We used min-max and max-min approaches to create a comparable index of attractiveness

for each variable in our data. The min-max and max-min approaches normalize the values of

each variable between 0 and 100, where 0 indicates the least attractive offshoring region and

100 the most attractive region for a firm. We use the following formula in the case that a

higher numerical value of a variable indicates a higher attractiveness of the location:

𝑋𝐼 = 100 𝑋− 𝑋𝑚𝑖𝑛

𝑋𝑚𝑎𝑥−𝑋𝑚𝑖𝑛 , (1)

where 𝑋𝐼 is the index value of an observation 𝑋 of a given variable, 𝑋𝑚𝑎𝑥 and 𝑋𝑚𝑖𝑛 give the

maximum and minium value of the variable, respectively. The following formula is used if

lower values indicate a higher attractiveness:

𝑋𝐼 = 100 𝑋− 𝑋𝑚𝑎𝑥

𝑋𝑚𝑖𝑛−𝑋𝑚𝑎𝑥 . (2)

For example, the lowest wage for IT employees is paid in Northern Bulgaria (on average 4

EUR per hour), and this observation will receive a value of 100. The highest wage for IT

employees is paid in Sweden (on average 40 EUR per hour) and consequently this

3 The Data Online Appendix is available at data.bzoller.com

https://ec.europa.eu/eurostat/web/regions/data/database


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observation receives a value of zero. Index values for observations between these two

extremes are linearly interpolated.

We used the survey results to assign weights that reflect the relative importance of each

variable within a pillar. As variables are linked to certain questions in the survey, we were

able to apply the rank sum method as proposed by Danielson & Ekenberg (2017) to compute

relative weights for each question and the corresponding variable based on the average

importance value given by the Swiss IT firms in the survey. Relying on the rank sum implies

that the rank order is directly reflected in the assigned weights. Specifically, the weight is

calculated using the following formula:

𝑤𝑖 = 100𝑁+1−𝑖

∑ (𝑁+1−𝑗𝑁𝑗 )

(3)

where 𝑤𝑖 indicates the weight of a question 𝑖 in a specific pillar. We ordered the questions

within each pillar by their average importance, with the question with the highest average

importance being indicated by 𝑖 = 1, the question with the second highest average

importance rating being 𝑖 = 2, and so on. Thus 𝑖 does not only indicate a specific question,

but also its rank within the pillar. 𝑁 gives the number of questions within a specific pillar. For

example, the economic pillar consists of 4 questions, 𝑁 = 4. The question with the highest

average importance rating refers to the access to local credits, 𝑖 = 1. According to the

formula, access to credit or the ease of obtaining credit variable has a weight of 40% within

the economic pillar. The second ranked question considers the general economic

environment (inflation and exchange rate stability) and, using the formula, yields a weight of

30%.

If more than one variable was assigned to a question, the importance weight was equally split

between all assigned variables. Thus, each pillar was computed as the weighted importance

of the underlying questions. Next, we weighted the pillars among each other to create the

overall IT Nearshoring Index. We conducted expert interviews to obtain weights for the

different pillars similar to our firm survey. Experts were asked to rank the relative importance

directly in percentage. Thus, in the presented IT Nearshoring Index, we do not use the rank

sum method to assign weights to each pillar, but, rather, the direct relative importance weights

provided by the experts. We feel confident that, after an extensive briefing of the experts,

their importance valuation is truly a reflection of the relative importance and not a rank

importance. Table 1 gives the relative importance weight directly obtained from the experts

and, for comparison, the corresponding rank sum weights for each pillar. In this case the rank

is based on the (unweighted) average importance of all questions that constitute the pillar.


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Table 1 | Expert rating of relative importance of each pillar.

Relative importance Rank sum weight

Economic pillar 17.35% 6.66%

Location pillar 19.91% 26.66%

Social pillar 19.21% 13.33%

Institutional pillar 19.76% 20.00%

Labour pillar 23.85% 33.33%

Source: authors

Using the rank sum weight gives, by assumption, a much smaller weight to the lowest ranked

pillar, in our case the economic pillar. This does not fully reflect the relative importance. As

mentioned above, the interviewed experts were aware of the significance of the relative

importance, while this fact was not clear for the participants of the survey. Using the rank sum

weights changes the order of regions in the IT Nearshoring Index only slightly, i.e. UK regions

will be even more dominant and Eastern European regions will be further down in the ranking.

In the end, the weighting approach gives us the relative importance of the previously identified

determinants of the nearshoring decision, which answers RQ2.

2 Pillar description

In this section we describe the 5 pillars of the IT Nearshoring Index in more detail and explain

why each pillar matters for Swiss IT service firms. We describe only selected variables but

mention all survey questions that constitute a pillar. We provide a more detailed description

of the variables and sources in an Online Appendix.4

2.1 Institutional pillar

The institutional pillar reflects political factors and their direct consequences for the offshoring

decision. For example, it includes the regional quality of government given by Charron et al.

(2015) to indicate ease of doing business and dealing with the government in that specific

region. We assume that better institutions decrease the costs of doing business in a region

as well as political uncertainty, thus creating a more stable economic environment.

In the survey, respondents were asked to rate the importance of IP protection and data

privacy laws, ease of doing business, openness towards foreign investment, and political

stability.

2.2 Location pillar

The location pillar refers more generally to the geographical factors of the decision to

nearshore. We include, for example, the distance in kilometers between the potential

nearshoring region and Bern – which is located in the center of Switzerland – or the number

of airport passengers in a region to reflect the reachability of a region. Egger et al. (2018)

have shown that physical distance and communication barriers represent obstacles for firms

4 The Data Online Appendix is available at data.bzoller.com

http://dx.doi.org/10.1007/s11205-014-0815-3

http://hdl.handle.net/20.500.11850/224571


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when engaging in international business. Variables reflecting transportation infrastructure

and the reachability of a region have the highest weight within this pillar.

In the survey, respondents were asked to rate the importance of geographical reachability of

a region, time zone, property rights, ICT infrastructure, language and communication, as well

as physical attractiveness of a region.

2.3 Economic pillar

The economic pillar considers direct economic measures. These can often be independent

of institutions. For example, we include labour and corporate income taxes in this pillar, which

are clearly an institutional/political outcome, but their value is not fully explained by

institutional factors, i.e., France has good and high-quality institutions and rather high

corporate and labour tax rates. On the other hand, institutions in The Netherlands are equally

good, but corporate tax rates are much lower. Our survey indicates that economic factors are

most important for Swiss IT firms in this pillar, thus the variables with the highest weight are

inflation and stability of the currency exchange rate.

In the survey, respondents were asked to rate the importance of corporate taxes, the general

economic environment, the market potential of the region, and access to financial markets.

2.4 Labour pillar

The labour pillar reflects various dimensions of the local labour market. Specifically, IT labour

supply and demand factors, as well as labour costs factors. Obviously, firms want to offshore

to destinations with a high supply of skilled IT workers, which makes it easier to fill open

vacancies. On the other hand, labour costs should be low, which raises the profitability of the

nearshoring venture. For Swiss IT service firms, the supply of skilled IT workers is much more

important than the actual labour costs. Thus, the labour market tightness and supply of IT

workers in a region receive the highest weights in this pillar.

In the survey, respondents were asked to rate the importance of availability of IT workers, the

quality of IT workers, and the labour costs in a region.

2.5 Social pillar

In the social pillar we consider all kinds of social factors that might affect a firm’s decision to

nearshore. Foremost, cultural distance is an important factor and an often underestimated

obstacle for firms operating in foreign markets. Research has shown that capabilities to use

and to adjust to cultural differences is firm-specific, however it was not possible to include

these firm-specific dimension in our general social pillar. Thus, we included a broader

measure of social factors at the regional level, as we assume that general cultural closeness

facilitates the cultural adjustments on average. Specifically, we include a cultural distance

measure of the region relative to Switzerland (as a whole country), which is taken from Kaasa

et al. (2013), or language proximity taken from Melitz & Toubal (2014). Note that as

Switzerland has four official languages and can be divided into 3 major cultural areas (French,

Italian, and German speaking regions), the cultural distance and language measures reflect

a Swiss average.

https://doi.org/10.1177/1470595813485379

https://doi.org/10.1177/1470595813485379


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43

In the survey, respondents were asked to rate the importance of cultural distance, personal

contacts, and personal safety.

3 Results

In this section, we first give a general overview of the data and the different pillars and present

the overall IT Nearshoring Index. We test the hypothesis from Section 1 and discuss how

Brexit would affect the attractiveness of British regions within our IT Nearshoring Index.

The IT Nearshoring Index consists of five pillars that describe specific dimensions of the

attractiveness of a possible nearshoring location for Swiss IT service firms. As all variables

that constitute a pillar are normalized to an index between 0 and 100, the weighted average

of these variables also has to be between 0 and 100. Table 2 shows the summary statistics

of the five pillars and the overall IT Nearshoring Index, where variables have been weighted

as described in Section 3 and the (relative importance) weights for the overall nearshoring

index are given in Table 1.

Table 2 | Summary statistics of the 5 pillars and the nearshoring index.

Economic pillar

Location pillar

Social pillar

Institutional pillar

Labour pillar

Overall index

Mean 58.16 60.95 48.62 59.73 46.00 54.23

Minimum 40.05 27.34 14.78 28.60 2.52 27.99

Maximum 73.06 84.92 74.52 86.48 74.15 68.99

Median 57.26 61.75 51.06 65.47 45.94 57.16

Variance 5.86 18.95 14.95 23.04 24.82 92.64

Obs. 115 115 115 115 115 115

Source: authors

Figure 2 | Whisker plot of the IT

Nearshoring Index and its 5 pillars.

Source: authors


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In general, we observe substantial regional heterogeneity within the five pillars and the overall

index. Figure 2 depicts a whisker plot for each of the pillars and the overall IT Nearshoring

Index. In the plot, the distribution of the region index values is described by a whisker for each

pillar. The rectangular box of the whisker gives the interquartile range of the underlying data

and the bold horizontal line indicates the median value. The longer the rectangular box, the

more dispersed the pillar index values between different regions. The upper and lower bound

of the whisker are given as 1.5 times the interquartile range and the dots are outlier NUTS 1

regions.

Especially in the labour, institutional, and location pillars, we see a considerable dispersion,

indicating that some NUTS 1 regions perform very well in these dimensions, while others do

not at all. In general, the institutional quality seems rather high (median is over 65), which is

not surprising as most countries are members of the European Union, which ensures certain

institutional standards. Similarly, the location pillar has a high average and median as

Switzerland is located very much in the center of Europe and geographical distance has a

high weight in the pillar. The labour pillar has the lowest mean and median and, as indicated

by the dots in the lower part, the most outliers of the 5 pillars. This is due to the very low

supply of IT workers in some rural regions, mainly in the European periphery.

Table 3 shows the Pearson correlation coefficient for the 5 pillars and the overall IT

Nearshoring Index. Clearly, and by construction, the overall nearshoring index is positively

correlated with all 5 pillars but the correlation between the different pillars varies considerably.

Some pillars are only weakly correlated, such as the economic pillar and the location pillar,

while others are very strongly correlated, such as the social and institutional pillar. This partly

reflects the interdependence of the pillars and the underlying data generating process

Although the pillars are not independent or orthogonal in a statistical sense, the limited

correlation between most pillars indicates that the different pillars indeed indicate different

factors of the nearshoring decision of Swiss IT firms.

Table 3 | Pearson correlation coefficient of the 5 pillars and the overall nearshoring index

Economic pillar

Location pillar

Social pillar


Labour pillar

Overall Index

Economic pillar 1.000 0.171 -0.036 0.118 0.294 0.326

Location pillar 0.171 1.000 0.800 0.817 0.322 0.881

Social pillar -0.036 0.800 1.000 0.731 0.110 0.735

Institutional pillar 0.118 0.817 0.731 1.000 0.346 0.872

Labour pillar 0.294 0.322 0.110 0.346 1.000 0.656

Overall Index 0.326 0.881 0.735 0.872 0.656 1.000

Source: authors

Figures 3 to 7 plot maps for each of the five pillars. Regions with high index scores in the

respective pillar are darker, while regions with low scores are lighter. Economic factors are

more favorable in Central Europe, the UK, and Eastern Europe, while firms face rather

unfavorable conditions in Southern Europe and France. Many of the regions in Southern

Europe still have not fully recovered from the European debt crisis in 2009, implying lower

market potentials in these regions. While the institutional factors are very good in most


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45

European countries, the Scandinavian countries stand out above all. On the other hand, many

South-Eastern European regions are unattractive for nearshoring as their institutions are

weaker by comparison to most other European regions. As already indicated in Table 3, the

location and social pillar are highly correlated. Specifically, regions that are geographically

closer to Switzerland have higher scores in the location and social pillar. This is consistent

with the findings of Argote & Ingram (2003) and Argote et al. (2012). In terms of the labour

market pillar, we observe the opposite; regions that are more peripheral are more competitive.

Eastern Europe and specifically Poland combine low wages with a large IT workforce, making

it very attractive for Swiss IT firms. This holds as well for countries such as Spain and

Portugal. We provide detailed interactive graphs and the complete ranking of the IT

Nearshoring Index in an Online Appendix.5

5 The interactive graphs Online Appendix is available at nearshoring.bzoller.com

Figure 3 | Map of economic pillar

Source: authors


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Source: authors

Source: authors

Figure 4 | Map of the institution pillar

Figure 5 | Map of the location pillar


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47

Figure 8 shows the overall IT Nearshoring Index. London and the surrounding regions are

most attractive for Swiss IT firms but Southern and Western German regions also rank very

high. In Southern Europe, Madrid and Catalonia (Barcelona) are favorable locations. Most

regions in Eastern and South-Eastern Europe are not very attractive nearshoring locations

for Swiss IT firms. In general, it seems that greater metropolitan areas such as London, Berlin,

Source: authors

Source: authors

.

Figure 6 | Map of the social pillar

Figure 7 | Map of the labour pillar


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Hamburg, or Madrid are more successful at attracting a sizable pool of IT workers, due at

least partly to the ease in reaching them. Tables 4 and 5 show the Top 10 regions of the

overall IT Nearshoring Index and the Top 10 Eastern European regions in the overall index,

respectively. By a wide margin, London is the most attractive location for Swiss IT service

firms in Europe. Most other regions on the Top 10 are rather close to each other. The highest

ranked Eastern European region is Central Poland in which Warsaw and Łódź located. These

are the biggest and third biggest cities in Poland. A good supply of skilled workers from local

universities, relatively low wages, and the access to an international airport make Central

Poland an attractive location for IT firms.

Table 4 | Top 10 regions to nearshore for Swiss IT service firms

Rank Region Country Overall Index

1 London United Kingdom 69

2 South East UK United Kingdom 65.2

3 Bavaria Germany 64.8

4 Berlin Germany 64.4

5 Denmark Denmark 64.4

6 East of England United Kingdom 64.4

7 Baden-Württemberg Germany 64.2

8 North Rhine-Westphalia Germany 64.1

9 Hamburg Germany 63.8

10 Ireland Ireland 63.7

Source: IT Nearshoring Index 2018, authors’ own calculations

Figure 8 | Map of the overall IT Nearshoring Index.

Source: authors


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49

Table 5 | Top 10 Eastern European regions to nearshore for Swiss IT service firms

Rank Region Country Overall Index

60 Central Poland Poland 56.1

62 Wschodni Poland 55.8

63 Poludniowy Poland 55.4

65 Pólnocno-Zachodni Poland 55.4

69 Poludniowo-Zachodni Poland 55.1

70 Slovenia Slovenia 54.6

73 Pólnocny Poland 54.0

75 Budapest Hungary 53.6

77 Slovakia Slovakia 53.2

78 Estonia Estonia 53.0

Source: IT Nearshoring Index 2018, authors’ own calculations

In our hypothesis we stated that the Eastern European countries are the most attractive

nearshoring locations for Swiss IT service firms. We used a standard student t-test to test our

hypothesis (see Table 6). It compares the means of the Top 10 regions of the overall IT

Nearshoring Index and the Top 10 Eastern European regions in each pillar. In 4 out of the 5

pillars, the Top 10 Eastern European regions’ average score is significantly lower than the

average of the Top 10 overall regions. The location and social pillar have the highest

difference. This does not come as a surprise: geographically Eastern European regions are

farther away and harder to reach than most Western European regions. The Top 10 Eastern

European regions perform well on the labour market dimension, where their average index

rank is not significantly different from the Top 10 overall regions. This is mainly based on the

labour cost advantage of Eastern European regions. The survey of Swiss IT firms indicates

that the supply of skilled IT workers is the most important factor for the outsourcing decision

of firms (average importance score of 6.33 out of 7). It is much more important than labour

costs, which have an average importance of 5.86. Thus, for most Eastern European regions

it will be more important to attract skilled IT workers than to compete with low labour costs.

Economic and institutional factors matter, but their overall impact on explaining the lower

attractiveness of Eastern European regions is relatively small. In this light, we can reject our

hypothesis that Eastern European countries/regions are the most attractive location for Swiss

IT service firms.


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Table 6 | Difference between Top 10 overall regions and the Top 10 Eastern European regions

Economic pillar

Location pillar

Social pillar


Labour pillar

Overall index

Top 10 (avg. index) 68.2 74.9 62.0 70.1 51.7 64.80

Top 10 EE (avg. index)

62.5 57.5 38.9 56.8 57.2 54.61

Difference 5.7 17.4 23.2 13.3 -5.5 10.18

t-value 3.360 10.037 9.845 4.454 -1.272 17.04

p-value 0.004 0.000 0.000 0.000 0.220 0.000

Difference between the Top 10 overall regions and Top 10 Eastern European regions based on the average index

values of the 5 pillars and the overall index. Two-sided student t-test, equal variance.

Source: IT Nearshoring Index 2018, authors’ own calculations.

Lastly, we can create a counterfactual IT Nearshoring Index to account for the impact of a

possible Brexit on the attractiveness of UK regions. Leaving the European Union would

decrease the IT Nearshoring Index score for every UK region by about 2.4 points. While

London would still be the most attractive location for Swiss IT service firms, the second

ranked South East UK region drops out of the Top 10 to rank 11. The ranking for the highest

ranked Central European region – Central Poland – would not increase at all after a possible

Brexit, as the lowest ranked UK region – Northern Ireland – is ranked 46 with an IT

Nearshoring Index score of 58.8. In general, we find that Brexit would hit the UK regions

which are already not ranked very high relatively harder than those that are ranked high.

The IT Nearshoring Index is consistent with common findings in the literature. Smite et al.

(2013) use case studies to identify different factors that influence the location decision of IT

service firms. Among the most important factors are labour costs and resource availability.

They also point out that not only geographical distance has a negative effect for the location

choice of IT firms, but also cultural distance. With our IT Nearshoring Index, we provide a

quantitative framework that incorporates all these factors. Thus, the IT Nearshoring Index

actually reflects the firm trade-offs described by Carmell & Abbott (2007), i.e. a multi-

dimensional measure of distance (geographical and cultural) is weighted against classical

economic factors such as wages and availability of workers. This is also reflected in the low

correlation between the economic pillar and the social pillar shown in Table 3. Moreover, we

explicitly consider within-country heterogeneity, which can be an important reason for

competitive advantages as pointed out by Abbott & Jones (2012) relying on two case studies.

Lastly, the weighting of the IT Nearshoring index is consistent with the empirical findings of

Ellram et al. (2013) and Egger et al. (2018). The former uses a regression analysis and find

that the factors with the highest loadings are the availability of local management and labour,

but also geographical distance and economic factors.

Conclusion

In this article we presented an IT Nearshoring Index for Swiss IT firms. We used a survey

among Swiss IT firms to identify 5 important determinants of the off- and nearshoring location

decision of IT firms in Switzerland. Based on the survey results, we evaluated the relative

importance of these determinants. The most important factor for Swiss IT firms is the regional

labour market for IT professionals. Specifically, the availability of skilled IT workers in a


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51

possible nearshoring destination ranks with the highest importance in the survey. The

reachability and cultural closeness are almost equally important for IT firms when choosing

their nearshoring location. Direct economic factors, such as the possible market size are less

important.

Based on these findings we constructed an IT Nearshoring Index and found that metropolitan

areas are the most attractive destinations for Swiss IT firms. We identified two attractiveness

clusters, the Southern UK around London and Western Germany. The former is due to the

high availability of IT workers, while the latter is due to the good reachability and social

closeness of the Southern German regions to Switzerland. Empirically we rejected our

hypothesis that Eastern European countries are the most attractive locations for Swiss IT

service firms. Although wages for IT workers in Eastern Europe are considerably lower than

in Western Europe, it is not enough to compensate for the vast geographical and cultural

distances of these regions. To increase the competitiveness of the region, governments could

further strengthen the education in IT and therefore enlarge the availability of IT workers.

Acknowledgement

Research assistantship of Sandra Hubmann, Florian Elias, and Yannic Egly is greatly

acknowledged.

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The research paper has been reviewed. | Received: May 29, 2019; Revised: August 2, 2019;

Accepted: August 22, 2019; Prepublished online: August 22, 2019; Published: October 1, 2019

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